Content descriptor

ABSTRACT

An apparatus, method, system and computer-readable medium are provided for generating one or more descriptors that may potentially be associated with content, such as video or a segment of video. In some embodiments, a teaser for the content may be identified based on contextual similarity between words and/or phrases in the segment and one or more other segments, such as a previous segment. In some embodiments, an optical character recognition (OCR) technique may be applied to the content, such as banners or graphics associated with the content in order to generate or identify OCR&#39;d text or characters. The text/characters may serve as a candidate descriptor(s). In some embodiments, one or more strings of characters or words may be compared with (pre-assigned) tags associated with the content, and if it is determined that the one or more strings or words match the tags within a threshold, the one or more strings or words may serve as a candidate descriptor(s). One or more candidate descriptor identification techniques may be combined.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.13/291,154, filed Nov. 8, 2011, entitled CONTENT DESCRIPTOR. The priorapplication is herein incorporated by reference in its entirety.

BACKGROUND

With the advent of social media and distribution of content via networkssuch as the Internet, users have access to innumerable pieces ofcontent. For example, websites (e.g., commercial websites) may provideaccess to videos that are uploaded by users, thereby allowing users toreach larger audiences with their content, and allowing users to accessthousands and thousands of videos. To help users find videos ofinterest, a creator of the video may define one or more keyword tags toaccompany the video and give prospective viewers some information onwhat the video contains.

A tag may be one or more words that identify the content of a particularprogram segment (e.g., the keyword “peanut” for a news story aboutpeanut farmers), and may provide a user with the ability to determinewhat the subject of the video is and whether the video is of interest.Unfortunately, it may be difficult for a user to distinguish videos. Forexample, tags are frequently inaccurate, and uploaders often chooseambiguous terms to describe videos. As a result, a user looking for avideo on a particular topic or subject of interest may have to siftthrough, or watch, a large number of videos that are not of interest.This disclosure identifies and addresses shortcomings related to anincreasing number of content items, such as videos, that becomeavailable. There is an ever-present need to improve a user's ability toquickly find content of interest.

SUMMARY

This summary is not intended to identify critical or essential featuresof the disclosure provided herein, but instead merely summarizes certainfeatures and variations thereof.

In some illustrative embodiments, content (e.g., a video) may be dividedup, or fragmented, into one or more segments. Segments may be, forexample, time-based portions of a program, such as the first 5 minutesof a show, or the portion between the 2^(nd) and 3^(rd) commercialbreaks, etc. Boundaries for the various segments may be based oncriteria, such as topical coherence criteria. The segments may havedifferent start and/or end times.

In one aspect, a descriptor may be applied to, or associated with, thecontent, such as one or more segments of the content. A descriptor mayinclude one or more characters. For example, a descriptor may includeone or more words or phrases, such as one or more sentences. In someembodiments, a descriptor may include audio or other types of data. Theaudio may be played in response to a user selection of the descriptor.For example, in some embodiments the descriptor may be presented as aselectable item, such as a link, a button, or the like.

A generation of a descriptor may be based on any number of inputs,factors, or conditions. For example, in some illustrative embodiments, adescriptor may be generated based at least in part on an identificationof a teaser. In some embodiments, the teaser may include material in afirst segment that is representative of material in a second segment. Insome embodiments, the second segment may be different from the firstsegment. In some embodiments, the second segment may be earlier in timeor later in time relative to the first segment. In some embodiments,similarity techniques may be used to determine whether material in aparticular segment qualifies as a teaser.

In some illustrative embodiments, a descriptor may be generated based atleast in part on an optical character recognition (OCR) technique. Forexample, text or characters associated with graphics, banners, or thelike included in a segment may be OCR'd and compared with one or moretags assigned to the segment. When a match exists beyond a threshold,the text or characters may serve as a descriptor.

In some illustrative embodiments, a descriptor may be generated based atleast in part on the use of a salient text detection. For example,words, terms, or phrases in a transcript (e.g., a closed captiontranscript) associated with a segment may be compared with one or moretags assigned to the segment. When a match exists beyond a threshold,the words, terms, or phrases may serve as a descriptor.

Other details and features will also be described in the sections thatfollow.

BRIEF DESCRIPTION OF THE DRAWINGS

Some features herein are illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements.

FIG. 1 illustrates an example network.

FIG. 2 illustrates an example platform on which the various elementsdescribed herein can be implemented.

FIG. 3 illustrates an example flow diagram demonstrating one or moreaspects of this disclosure.

FIGS. 4-6 illustrate example methods demonstrating one or more aspectsof this disclosure.

FIG. 7 illustrates an example transcript in accordance with one or moreaspects of this disclosure.

DETAILED DESCRIPTION

Various connections between elements are discussed in the followingdescription. These connections are general and, unless specifiedotherwise, may be for example direct or indirect, wired or wireless, andthis specification is not intended to be limiting in this respect.

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

FIG. 1 illustrates an example network 100 on which many of the variousfeatures described herein may be implemented. Network 100 may be anytype of information distribution network, such as satellite, telephone,cellular, wireless, etc. One example may be an optical fiber network, acoaxial cable network or a hybrid fiber/coax distribution network. Suchnetworks 100 use a series of interconnected communication lines 101(e.g., coaxial cables, optical fibers, wireless, etc.) to connectmultiple premises 102 (e.g., businesses, homes, consumer dwellings,etc.) to a central office or headend 103. The central office 103 maytransmit downstream information signals onto the lines 101, and eachpremises 102 may have a receiver used to receive and process thosesignals.

There may be one line 101 originating from the central office 103, andit may be split a number of times to distribute the signal to variouspremises 102 in the vicinity (which may be many miles) of the centraloffice 103. The lines 101 may include components not illustrated, suchas splitters, filters, amplifiers, etc. to help convey the signalclearly, but in general each split introduces a bit of signaldegradation. Portions of the lines 101 may also be implemented withfiber-optic cable, while other portions may be implemented with coaxialcable, other lines, or wireless communication paths.

The central office 103 may include a termination system (TS) 104, suchas a cable modem termination system (CMTS), which may be a computingdevice configured to manage communications between devices on thenetwork of lines 101 and backend devices such as servers 105-107 (to bediscussed further below). The termination system (TS) may be asspecified in a standard, such as the Data Over Cable Service InterfaceSpecification (DOCSIS) standard, published by Cable TelevisionLaboratories, Inc. (a.k.a. CableLabs), or it may be a similar ormodified device instead. The termination system (TS) may be configuredto place data on one or more downstream frequencies to be received bymodems at the various premises 102, and to receive upstreamcommunications from those modems on one or more upstream frequencies.The central office 103 may also include one or more network interfaces108, which can permit the central office 103 to communicate with variousother external networks 109. These networks 109 may include, forexample, networks of Internet devices, telephone networks, cellulartelephone networks, fiber optic networks, local wireless networks (e.g.,WiMAX), satellite networks, and any other desired network, and theinterface 108 may include the corresponding circuitry needed tocommunicate on the network 109, and to other devices on the network suchas a cellular telephone network and its corresponding cell phones 117.

As noted above, the central office 103 may include a variety of servers105-107 that may be configured to perform various functions. Forexample, the central office 103 may include a push notification server105. The push notification server 105 may generate push notifications todeliver data and/or commands to the various premises 102 in the network(or more specifically, to the devices in the premises 102 that areconfigured to detect such notifications). The central office 103 mayalso include a content server 106. The content server 106 may be one ormore computing devices that are configured to provide content to usersin the homes. This content may be, for example, video on demand movies,television programs, songs, text listings, etc. The content server 106may include software to validate user identities and entitlements,locate and retrieve requested content, encrypt the content, and initiatedelivery (e.g., streaming) of the content to the requesting user and/ordevice.

The central office 103 may also include one or more application servers107. An application server 107 may be a computing device configured tooffer any desired service, and may run various languages and operatingsystems (e.g., servlets and JSP pages running on Tomcat/MySQL, OSX, BSD,Ubuntu, Redhat, HTML5, JavaScript, AJAX and COMET). For example, anapplication server may be responsible for collecting television programlistings information and generating a data download for electronicprogram guide or interactive program guide listings. Another applicationserver may be responsible for monitoring user viewing habits andcollecting that information for use in selecting advertisements. Anotherapplication server may be responsible for formatting and insertingadvertisements in a video stream being transmitted to the premises 102.Another application server may be responsible for receiving user remotecontrol commands, and processing them to provide an intelligent remotecontrol experience.

An example premises 102 a may include an interface 120. The interface120 may comprise, in one aspect, a modem 110, which may includetransmitters and receivers used to communicate on the lines 101 and withthe central office 103. The modem 110 may be, for example, a coaxialcable modem (for coaxial cable lines 101), a fiber interface node (forfiber optic lines 101), or any other desired modem device. The modem 110may be connected to, or be a part of, a gateway interface device 111.The gateway interface device 111 may be a computing device thatcommunicates with the modem 110 to allow one or more other devices inthe premises 102 a to communicate with the central office 103 and otherdevices beyond the central office. The gateway 111 may be a set-top box(STB), digital video recorder (DVR), computer server, or any otherdesired computing device. The gateway 111 may also include (not shown)local network interfaces to provide communication signals to devices inthe premises 102 a, such as televisions 112, additional STBs 113,personal computers 114, laptop computers 115, wireless devices 116(wireless laptops and netbooks, mobile phones, mobile televisions,personal digital assistants (PDA), etc.), and any other desired devices.Examples of the local network interfaces include Multimedia Over CoaxAlliance (MoCA) interfaces, Ethernet interfaces, universal serial bus(USB) interfaces, wireless interfaces (e.g., IEEE 802.11), Bluetoothinterfaces, and others.

FIG. 2 illustrates general hardware elements that can be used toimplement any of the various computing devices discussed above. Thecomputing device 200 may include one or more processors 201, which mayexecute instructions of a computer program to perform any of thefeatures described herein. The instructions may be stored in any type ofcomputer-readable medium or memory, to configure the operation of theprocessor 201. For example, instructions may be stored in a read-onlymemory (ROM) 202, random access memory (RAM) 203, removable media 204,such as a Universal Serial Bus (USB) drive, compact disk (CD) or digitalversatile disk (DVD), floppy disk drive, or any other desired electronicstorage medium. Instructions may also be stored in an attached (orinternal) hard drive 205. The computing device 200 may include one ormore output devices, such as a display 206 (or an external television),and may include one or more output device controllers 207, such as avideo processor. There may also be one or more user input devices 208,such as a remote control, keyboard, mouse, touch screen, microphone,etc. The computing device 200 may also include one or more networkinterfaces, such as input/output circuits 209 (such as a network card)to communicate with an external network 210. The network interface maybe a wired interface, wireless interface, or a combination of the two.In some embodiments, the interface 209 may include a modem (e.g., acable modem), and network 210 may include the communication lines 101discussed above, the external network 109, an in-home network, aprovider's wireless, coaxial, fiber, or hybrid fiber/coaxialdistribution system (e.g., a DOCSIS network), or any other desirednetwork.

Aspects of this disclosure may be implemented to generate and apply oneor more descriptors to one or more segments of content. For purposes ofillustrative simplicity and consistency, the content is described asincluding video (e.g., video clips associated with a news program). Thetechniques described herein can be adapted and applied to other forms ortypes of content, such as text files, audio (e.g., music), etc.

FIG. 3 illustrates a flow diagram suitable for demonstrating one or moreaspects of this disclosure. In some embodiments, the various blocksshown in FIG. 3 may be included in, associated with, or executed inaccordance with one or more of the components and devices describedabove in connection with FIGS. 1-2. The flow diagram of FIG. 3 may beused to generate (e.g., automatically generate) one or more descriptorsfor one or more segments of data, such as segments of video. The one ormore segments of video might not have a descriptor associated with themyet, or might only have a few tags that may be elaborated on.

The flow diagram of FIG. 3 may follow steps where a video 300 (e.g., anightly news program) has been broken down into time-based segments(e.g., segments separated by commercial breaks). The video 300 may beobtained from one or more sources, such as one or more network orcontent providers, and the source(s) may provide information indicatingwhere the segments begin and end. The source(s) may also supply one ormore tags describing the segments. For example, keyphrase extraction maybe used by the source(s) to generate the one or more tags, and thekeyphrase extraction may be based at least in part on statistics (e.g.,frequency of occurrence of a given word in a segment). One skilled inthe art would understand how to segment and generate and apply tagsfor/to a video, and as such, a further description of these techniquesis omitted from this disclosure for the sake of brevity.

As shown in FIG. 3, a transcript 302 (or any descriptive file describingthe program and/or its segments) may also be obtained or received forthe video 300 and its segments. The transcript may be based, forexample, on one or more closed captioning processes. The transcript mayinclude a textual transcription of spoken or audible words or phrasesthat are heard during the video and its segments.

The transcript 302 may be processed by one or more techniques toidentify portions of the transcript (e.g., a spoken sentence or phrase)that can serve as proposed descriptions (e.g., descriptors) for eachsegment in the video. Three descriptor generation techniques are shownin FIG. 3: salient tag detection 304, teaser detection 310, and OCRdetection 316. These techniques are exemplary and other types oftechniques may be used. These techniques may each process the datasegments, such as segments of a program (e.g., the teaser detection 310and salient tag detection 304 may process the transcript, while the OCRdetection 316 may process the video images contained in the videosegments, or vice versa), and may each provide one or more candidatedescriptors for a given segment. For example, candidate descriptor(s)308, 314 and 320 may generate one or more candidate descriptors in someembodiments. These candidates may then be processed together to selectone or more that will serve as the descriptor for the given segment.

Beginning with the salient tag detection 304, the transcript may beprocessed to identify one or more keywords or tags for each segment inthe program. As noted above, these keywords or tags may be supplied bythe content source, and they may be generated using any desiredkeyphrase extraction technique. The resulting set of keywords or tagsmay then be used in step 306 to filter the transcript's sentences. Inthis filtering, the contextual relationship strength between the tagsand each candidate descriptor may be determined. For example, acandidate descriptor may contain a plurality of words in a portion ofthe program, and each of those words can be compared against each of thetags or keywords. This comparison can be made against an overallcontextual database that lists, for each word in the database, one ormore other words that have been determined to be contextually related,with a relationship strength for each listed word. So, for example, theword “dog” may have an entry that lists the words “leash” and “park”,since dogs are often contextually related to leashes and parks. Therelationship strength value for “leash” may be higher (stronger) than“park,” though, since discussions using the term “leash” will almostalways also refer to a dog, and since discussions using the term “park”will sometimes relate to dogs, but will often have nothing to do withdogs. So while the dog's entry for “leash” may have a high value (e.g.,90), the dog's entry for “park” may be lower (e.g., 40).

In step 306, the tags found in salient tag detection 304 may be comparedwith each of the terms in the transcript, and the terms (or phrases)that have a contextual match exceeding a predetermined minimum strengthlevel may be identified as candidate descriptors 308. In someembodiments, the filtering can be conducted on a per-sentence basis,such that individual strength values for words or phrases in a singlesentence are combined and collectively compared to the minimum strengthlevel. Sentences meeting or exceeding the minimum strength value maythen be provided as candidate descriptors 308 for the segments in whichthey appear.

A second algorithm for identifying candidate descriptors can involveteaser detection 310. Teaser detection 310 may be used to determinewhether an earlier program segment contains a teaser or a trailer thatdescribes a later segment. An example teaser can be found in some newsprograms, when a news anchor gives a short summary, prior to acommercial break, of a news item that will be addressed following thecommercial break (e.g., “Stay tuned after the break, and we will tellyou about something in your house that may be killing you.”). The lastsentence of the earlier segment (“something in your house that may bekilling you”), may contain a helpful description of the content in thelater segment (just after the commercial break). To determine whether anearlier segment contains a teaser for a later segment, words, terms, orphrases included in an earlier segment of video may be compared with oneor more words, terms, or phrases from the later segment of the video todetermine whether one or more words, terms, or phrases are contextuallyrelated to, or descriptive of, the words, terms, or phrases included inthe second segment.

For example, if a given segment of a program contains lines 100-150 ofthe transcript, the teaser detection 310 may process some or all of thepreceding sentences in the transcript (e.g., sentences 1-99) todetermine if any of those earlier sentences could be teasers for thegiven segment. To do so, the teaser detection 310 algorithm can processeach of the earlier sentences (e.g., sentences 1-99), and identify thecontextual relationship strength between the words in those earliersentences with the words in the given segment's sentences (e.g.,identify the contextual relationship strength of each of sentences 1-99with sentences 100-150). This may result in a sentence relationshipstrength score for each of the earlier sentences 1-99, identifying howcontextually related those sentences are to the given segment and itssentences 100-150. One or more of the earlier sentences whoserelationship strength exceeds a predetermined minimum may be selected aspossible descriptors for the given segment.

The earlier sentence's relationship strength need not be the only factorin determining whether an earlier sentence is a possible teaser for thelater given segment. Another factor may be the contextual relationshipthat the earlier sentence has with its neighboring sentences in theearlier segment of the program, and the relevance interval of thesentence in the earlier segment. Stated differently, a teaser sentenceoften will be unrelated to nearby sentences, or will only be related toa small number of nearby sentences. If a sentence is related to a largenumber of nearby sentences, then it is less likely that the sentence wasactually a teaser for a different segment. So, for example, if sentence64 had a strong match to the segment in sentences 100-150, but sentence64 is also closely related to sentences 35-65, then it is less likelythat sentence 64 was truly a teaser for sentences 100-150. In thatsituation, the match between sentence 64 and sentences 100-150 may havebeen just a coincidence. On the other hand, if sentence 64 was onlyclosely related to one or two nearby sentences (e.g., sentences 63-66),then sentence 64 is more likely to have been a teaser for the sentences100-150 that it matched. In this proximity analysis, the teaserdetection algorithm may use a maximum relevance interval (e.g., acandidate teaser sentence can only be closely related to 6 nearbysentences, and if it is closely related to more than 6, then discard itas a candidate teaser) and a proximity relevance strength minimum (e.g.,a sentence is “closely” related to a neighboring sentence if the totalrelationship strength between the words in the two sentences exceeds anaverage strength of 90).

As another factor, the position of the earlier sentences within theirown segments may be taken into consideration in determining whether theyare possible teaser sentences. For example, if an earlier sentence isclose to the end of its respective segment, then it is more likely to bea teaser for the following segment. To make this determination, theteaser detection 310 may establish a distance value from the end of thesegment (e.g., 2 sentences from the end of the earlier segment), andpossible teaser sentences that are within that distance value of the endof the segment may be deemed more likely to be a teaser sentence. Insome embodiments, the various factors above can serve to increase ordecrease a value indicating the likelihood that a particular earliersentence is a teaser for a later segment. For example, and as describedabove, in some embodiments it may be more likely for a teaser to belocated just before (or within a threshold distance in terms of numberof words, terms, or phrases before) a commercial break (e.g., in orderto capture a viewing audience's interest or prompt the audience tocontinue watching the program after the commercial break has ended). Forexample, if a first segment is being examined as a potential orcandidate teaser for a second segment, and a commercial break resides inbetween the first and second segments, the last ‘n’ sentences of thefirst segment may be examined or the last ‘z’ seconds of the firstsegment may be examined, where ‘n’ and ‘z’ are numbers, such as 2 and10, respectively. A word, term, or phrase from the first segment locatedtowards the end of the first segment and just prior to a commercialbreak may receive added weight as a candidate teaser relative to anotherword, term, or phrase from the first segment located towards thebeginning or middle of the first segment and further from the commercialbreak.

This discussion above has assumed that teaser sentences for a givensegment can only occur in earlier sentences in the program. For example,in a newscast, a news anchor may preview or foreshadow a topic that isgoing to be discussed in greater detail during a future portion of theprogram (where the future portion of the program corresponds to thesecond segment) at a current point in time during the program (where thecurrent point in time corresponds to the first segment). In otherembodiments, a teaser sentence for a given segment can occur later inthe program, such as in a recap segment at the end of a news program,where the recap provides a short summary of an earlier story that wasdiscussed in the program.

The teaser detection 310 may result in a number of earlier sentencesidentified as possible teasers for the later segment. In step 312, thosecandidate teaser sentences can be filtered using the tags detected inthe salient tag detection 304. The contextual relationship strengthbetween each candidate teaser sentence (or the words in the sentence)and the tags for the later segment may be compared against apredetermined minimum strength value, and sentences that do not have astrong enough relationship to the tags may be filtered out. Theremaining candidate teasers can then be supplied as candidatedescriptors 314.

A third process, e.g., optical character recognition (OCR) detection 316may be used to generate a set of descriptor candidates based on text,graphics, and/or banners appearing in an image portion of a givensegment. In this OCR detection 316, the onscreen images of a givensegment may be analyzed with OCR of words and phrases appearing onscreen during that segment. This may be useful in identifyingdescriptors since some programs may run a textual banner on the screenin conjunction with the transmission (e.g., the broadcast, multicast,unicast, etc.) of the segment (e.g., a news story about a local sportsteam may have a banner across the bottom saying “Local Team Wins CityChampionship”).

In some embodiments, the filtering of the OCR text/characters may bebased at least in part on restricting the OCR text/characters to asubset of banners or graphics associated with the segment. For example,the OCR text/characters may be restricted to the first ‘Y’banners/graphics appearing in the segment, where ‘Y’ may be a number. Insome embodiments, the restriction imposed with respect to the ‘Y’banners/graphics appearing in the segment may be offset relative toanother position (e.g., the start) of the segment. For example, if ‘Y’is equal to six, and an offset of three is desired relative to the startof the segment, then the OCR text/characters may be restricted to thefourth through ninth banners/graphics appearing in the segment.

In some embodiments, knowledge of the program or segment may be used toidentify a set of candidate descriptors. For example, the Pardon TheInterruption (PTI) program on ESPN may include a set of topic bannerslocated on screen, where the current topic is highlighted andupcoming/future topics that are to be discussed are shown but nothighlighted. Knowledge of the structure and highlighting used inconnection with the topic banners may facilitate selection of the topicbanners as descriptors of a given segment of the program.

The OCR detection 316 for a given segment may intentionally ignoreonscreen text that appears at the very beginning or very end of thesegment (e.g., in the first or last 3 seconds), since it is possiblethat such onscreen text actually belongs to a neighboring segment in theprogram. For this purpose, an end- or beginning-buffer time period maybe used to define how much of the beginning and/or end of the segmentshould be ignored before conducting the OCR detection 316.

The OCR detection 316 may result in a number of potential OCRdescriptors, and these may be compared 318 against tags assigned to thegiven segment to facilitate the comparison. The use of a comparisonbetween the OCR text/characters and the tags may help to eliminate orfilter-out OCR text that is off-topic. For example, in the context of anews program, banners or graphics that may be present may change slowlybetween segments, such that a banner or graphic that is present in agiven segment may, at least for a short time (which may be evaluatedagainst a predetermined threshold amount of time), be representative ofthe previous segment. Comparing the OCR text/characters to the tags mayhelp to ensure that OCR text/characters indicative of a prior segmentare not considered as a candidate descriptor of the given segment. If itis determined that the OCR text/characters do not match the tags basedon the comparison, the OCR text/characters may be ignored or discardedas a possible descriptor. On the other hand, if it is determined thatthe OCR text/characters match one or more tags based on the comparison,the OCR text/characters may be retained as a possible descriptor.

The OCR text/characters generated by OCR detection 316 (potentially lessany text/characters that may be excluded by filtering as describedabove) may be provided to candidate descriptor(s) 320.

In some embodiments, variations on the foregoing descriptor generationtechniques may be used. For example, in some embodiments, OCR text orcharacters may be examined for a match with portions of transcript 302or one or more words, terms, or phrases included in a segment (by, e.g.,OCR detection 316), and if a match is found, the process may end. On theother hand, if a match is not found using the OCR text or characters (orif the number of matches is less than some specified number), thetranscript 302 or the one or more words, terms, or phrases may becompared (by, e.g., salient tag detection 304) to tags assigned to thesegment.

The descriptor generation techniques may be evaluated based on a varietyof terms or measures. For example, the descriptor generation techniquesmay be evaluated in terms of: (1) precision, or a measure of theaccuracy or quality of descriptors assigned relative to segments thathave been assigned descriptors, (2) recall, or a measure of the accuracyor quality of descriptors assigned relative to all (or a subset of) thesegments, (3) an F-score, or a combination of precision and recall,and/or (4) coverage, or the number of segments that are assigned adescriptor relative to all (or a subset of) the segments. As skilledartisans would appreciate, an F-score may be based on a weighted averageof precision and recall. The F-score may adhere to a formula of the form(2*P*R)/(P+R), where ‘P’ reflects precision and ‘R’ reflects recall. Insome embodiments, a weighting of precision relative to recall in theformula may be used to give importance or priority to precision relativeto recall (or vice versa).

In some embodiments, the two main measures taken into consideration maybe precision and coverage. Frequently, a tradeoff may need to be madebetween precision and coverage. For example, in order to increasecoverage, it may be necessary to reduce precision. Conversely, in orderto increase precision, it may be necessary to reduce coverage. Thechoice of how to weight precision relative to coverage may be based onproduct or service considerations, or stated in a slightly differentway, how one values precision relative to coverage.

In terms of evaluating precision, or more generally the quality oraccuracy of a descriptor assigned to a segment, a poll or survey ofusers may be conducted. The users may be asked to provide feedback as tothe quality of the assigned descriptor (e.g., based on some scale, suchas a scale providing options of “good”, “fair”, or “poor” forselection). The poll/survey may then be repeated for a relatively largenumber of segments (e.g., ninety segments) and descriptors, where thecorpus of segments may be selected to be indicative of the types ofvideos to be assigned descriptors going forward. In this manner, theperformance of the descriptor generation techniques can be measured orevaluated relative to one another, and adjustments can be made toachieve any number of objectives, such as maximizing one or more ofprecision and coverage (or a combination thereof). In some embodiments,the measurement or evaluation that may be performed may be adjusted,tailored, or modified based on the type of program or segment that isbeing examined. For example, an evaluation may be undertaken todetermine what algorithm (or combination(s) of algorithms) generate thebest results for different types of programs or segments. The determinedalgorithm(s) may then be applied to future segments of that same type.

While discussed above as separate descriptor generation techniques, insome embodiments features from two or more of the descriptor generationtechniques may be combined. For example, a high-precision, low-coveragedescriptor generation technique may be combined with a lower-precision,higher-coverage descriptor generation technique in an effort to increasecoverage while only obtaining slightly lower-precision. Referring toFIG. 3, two combination techniques 326 are illustrated: cascade 326 aand merge 326 b.

In the cascade technique 326 a, the candidates from one or moredescriptor generation techniques may be examined in turn based on apredetermined ordering or sequence of the descriptor generationtechniques. In some embodiments, the sequence or ordering of theexamination performed via the cascade technique 326 a may vary, or be afunction of, the type of program or segment that is being considered.The examination associated with cascade technique 326 a may stop orcease once a predetermined number of descriptors (e.g., a predeterminednumber of sentences or snippets) is/are found, or the process runs outdescriptor generation techniques (e.g., no more descriptor generationtechniques are available). In the merge technique 326 b, all (or asubset of) the candidates generated by the one or more descriptorgeneration techniques may be examined together as descriptors for asegment.

All other things being equal, the merge technique 326 b will have equalor higher precision than the cascade technique 326 a because the mergetechnique 326 b will include the descriptor(s) resulting from thecascade approach. All other things being equal, the merge technique 326b will utilize the same amount or a greater number of processingresources relative to the cascade technique 326 a, at least insofar asthe merge technique 326 b will present an equal or larger set ofcandidate descriptors to an editor for selection. Thus, in someembodiments where one of the combination techniques is being considered,a tradeoff may be made between precision and processing resources.

In some embodiments, combination technique 326 may include techniquesother than cascade 326 a and/or merge 326 b. For example, combinationtechnique 326 may simply pass or transmit one or more candidatedescriptors received from 308, 314 and/or 320.

The results or candidate descriptors may be presented to a user (e.g.,an editor) as final segment descriptor(s) 332. The user may correctspelling errors, capitalization or the like, which may be a partialresult of an error in transcript 302. The user may also assess theaccuracy or precision of the final descriptor(s) 332 and may select oneor more of the descriptor(s) (possibly after modification or refinementby the user) to be assigned to the segment under consideration. In someembodiments, if no candidate descriptor is generated, or if all thecandidate descriptors are deemed to be of low quality by the user, theuser may take one or more actions, including: (1) writing his/her owndescriptor for the segment, (2) not assigning any descriptor to thesegment, and/or (3) using any available tags as the descriptor.

As shown in the flow of FIG. 3, in some embodiments one or more ofcandidate descriptors 308, 314, and 320 may directly feed or provideinput to final segment descriptor(s) 332. For example, in embodimentswhere only one of salient tag detection 304, teaser detection 310, andOCR detection 316 is present or used, combination 326 might not be used.

FIGS. 4-6 illustrate methods that may be used in connection with theenvironments described above in connection with FIGS. 1-3. Forconvenience, but in a non-limiting manner, the methods of FIGS. 4-6 aredescribed below in connection with the flow of FIG. 3. In someembodiments, the methods of FIGS. 4-6 may execute in connection withdetection processes different from those described herein. The FIG. 4method may result in identifying candidate descriptors based on analysisof a program's transcript to compare words and phrases found on eitherside of a segment boundary; the FIG. 5 method may result in identifyingcandidate descriptors based on analysis of a program's video imagesusing optical character recognition (OCR); and the FIG. 6 method mayresult in identifying candidate descriptors based on comparing a programsegment's transcript with tag words received with the transcript.

The method of FIG. 4 may be used as the teaser detection 310, to resultin input provided to candidate descriptor(s) 314. In step 402descriptive data, such as a transcript (e.g., transcript 302) of a videomay be received. The descriptive data may correspond to segments of thevideo, and a portion of the descriptive data corresponding to a firstsegment of the video may be examined to determine if one or more words,terms, or phrases included therein may serve as a candidate descriptorof a second segment of the video.

In step 408, a variable ‘i’ may be set to zero for purposes ofinitialization. In some embodiments, the variable ‘i’ may correspond toa counter and may be used to iterate over the words, terms, or phrasesincluded in second segment of video. Although not shown in FIG. 4, insome embodiments, a memory or register associated with candidatedescriptor(s) 314 may also be cleared in step 408 so as to delete anystale or left-over descriptors from a prior execution of the method.

In step 414, the variable ‘i’ may be incremented. In regards to the flowfrom step 408 to step 414, the incrementing of ‘i’ may correspond topreparing to examine the first word, term, or phrase included in thefirst segment. In regards to the flow from step 432, the incrementing of‘i’ in step 414 may correspond to preparing to examine the next word,term, or phrase included in the first segment.

In step 420, a comparison may be made between the ‘i’th word, term, orphrase included in the first segment and the portion of the descriptivedata associated with the second segment of video to determine whetherthe ‘i’th word, term, or phrase included in the first segment iscontextually related to, or descriptive of, the second segment of video.If it is (e.g., the “YES” path is take out of step 420), then flow mayproceed to step 426, where that contextually related word, term orphrase may be provided as candidate descriptor(s) 314, and then the flowmay proceed to step 432. If the ‘i’ th word, term, or phrase included inthe first segment is not contextually related to, or descriptive of, thesecond segment of video (e.g., the “NO” path is taken out of step 420),the flow may proceed to step 432.

In step 432, a determination may be made whether the end (or some otherpredetermined point) of the first segment has been reached. If the endof the first segment has been reached (e.g., the “YES” path is taken outof step 432), then flow may proceed to step 438 and the method may end.Otherwise, if not at the end of the first segment (e.g., the “NO” pathis taken out of step 432), then flow may proceed back to step 414 toincrement the variable ‘i’ in order to prepare to examine whether thenext word, term, or phrase included in the first segment is atrailer/descriptor of the second segment of video.

While the method of FIG. 4 was described above as sequencing from thestart to the end of the first segment, in some embodiments thesequencing may proceed in a different order. For example, the sequencingmay start at the end of the first segment and proceed towards thebeginning of the first segment (potentially ending when reaching thestart of the first segment, or after some number of words, phrases, orsentences of the first segment have been examined). In some embodiments,multiple segments may be examined.

The method of FIG. 5 may be used as the OCR detection 316, to provideinput to candidate descriptor(s) 320. In step 504 descriptive data, suchas a transcript (e.g., transcript 302) of a video may be received. Thedescriptive data may correspond to segments of the video, and a portionof the descriptive data corresponding to a segment of the video may beexamined as described below. The descriptive data or the video segmentsmay have one or more assigned or associated tags.

In step 510, the images of a video program may be processed with OCRsoftware to identify text, graphics, and/or banners included in thesegment may be subject to an OCR technique or process. The text,graphics, and/or banners may be configured to be displayed when thecorresponding video is shown on a display screen or other device.

In step 516, a variable ‘i’ may be set to zero for purposes ofinitialization. In some embodiments, the variable ‘i’ may correspond toa counter and may be used to iterate over the OCR'd text, graphics,banners and the like. Although not shown in FIG. 5, in some embodiments,a memory or register associated with candidate descriptor(s) 320 may becleared in step 516 so as to delete any stale or left-over descriptorsfrom a prior execution of the method.

In step 522, the variable ‘i’ may be incremented. In regards to the flowfrom step 516 to step 522, the incrementing of ‘i’ may correspond topreparing to examine the first OCR'd text portion, graphic, or banner.In regards to the flow from step 516 to step 522, the incrementing of‘i’ may correspond to an increase by a number greater than one. Forexample, incrementing by more than one may help to ensure that stalegraphics/banners from a previous segment are not considered. In regardsto the flow from step 540, the incrementing of ‘i’ in step 522 maycorrespond to preparing to examine the next OCR'd text portion, graphic,or banner.

In step 528, the ‘i’ th OCR'd text portion, graphic, or banner may becompared against one or more tags that have been assigned to, orassociated with, the segment containing the OCR'd text portion, graphic,or banner. If the ‘i’ th OCR'd text, graphic, or banner is determined tobe contextually related to the tags (e.g., the “YES” path is taken outof step 528), then flow may proceed to step 534 where the ‘i’ th OCR'dtext, graphic, or banner may be provided as candidate descriptor(s) 320.In some embodiments, upon adding the ‘i’ th OCR'd text, graphic, orbanner as a candidate descriptor in connection with step 534, executionof the method of FIG. 5 may end. In some embodiments, the method maycontinue and flow may proceed from step 534 to step 540 as shown in FIG.5. If it is determined that the ‘i’ th OCR'd text, graphic, or banner isnot contextually related to the tags (e.g., the “NO” path is taken outof step 528), then flow may proceed to step 540.

In step 540, a determination may be made whether the end (or some otherpredetermined threshold) of the OCR'd text, graphics, and/or banners hasbeen reached. If the end has been reached (e.g., the “YES” path is takenout of step 540), then flow may proceed to step 546 and the method mayend. Otherwise, if not at the end (e.g., the “NO” path is taken out ofstep 540), then flow may proceed back to step 522 to increment thevariable ‘i’ in order to prepare to examine whether the next OCR'd textportion, graphic, or banner is contextually related to, or descriptiveof, the segment.

The method of FIG. 6 may be used as salient tag detection 304 to, e.g.,provide input to candidate descriptor(s) 308. In step 606 descriptivedata, such as a transcript (e.g., transcript 302) of a video may bereceived. The descriptive data may correspond to segments of the video,and a portion of the descriptive data corresponding to a segment of thevideo may be examined to determine if one or more words, terms, orphrases included therein may serve as a candidate descriptor of the samesegment. The descriptive data or the video segments may have one or moreassigned or associated tags.

In step 618, a variable ‘i’ may be set to zero for purposes ofinitialization. In some embodiments, the variable ‘i’ may correspond toa counter and may be used to iterate over the words, terms, or phrasesincluded in the descriptive data corresponding to the segment beingexamined. Although not shown in FIG. 6, in some embodiments, a memory orregister associated with candidate descriptor(s) 308 may also be clearedin step 618 so as to delete any stale or left-over descriptors from aprior execution of the method.

In step 624, the variable ‘i’ may be incremented. In regards to the flowfrom step 618 to step 624, the incrementing of ‘i’ may correspond topreparing to examine a first word, term, or phrase. In regards to theflow from step 642, the incrementing of ‘i’ in step 624 may correspondto preparing to examine the next word, term, or phrase.

In step 630, the ‘i’ th word, term, or phrase may be compared againstone or more tags that have been assigned to, or associated with, thesegment. If the ‘i’ th word, term, or phrase is determined to becontextually related to the tags (e.g., the “YES” path is taken out ofstep 630), then flow may proceed to step 636 where the ‘i’ th word,term, or phrase may be provided to candidate descriptor(s) 308. In someembodiments, upon adding the ‘i’ th word, term, or phrase as a candidatedescriptor in connection with step 636, execution of the method of FIG.6 may end. In some embodiments, the method may continue and flow mayproceed from step 636 to step 642 as shown in FIG. 6. If it isdetermined that the ‘i’ th word, term, or phrase is not contextuallyrelated to the tags (e.g., the “NO” path is taken out of step 630), thenflow may proceed to step 642.

In step 642, a determination may be made whether the end (or some otherpredetermined threshold) of the words, terms, and phrases has beenreached. If the end has been reached (e.g., the “YES” path is taken outof step 642), then flow may proceed to step 648 and the method may end.Otherwise, if not at the end (e.g., the “NO” path is taken out of step642), then flow may proceed back to step 624 to increment the variable‘i’ in order to prepare to examine whether the next word, term, orphrase is contextually related to, or descriptive of, the segment.

The methods described above in connection with FIGS. 4-6 areillustrative. In some embodiments, some of the steps (or portionsthereof) may be optional. Some of the steps (or portions thereof) may beexecuted in an order or sequence different from that shown. In someembodiments, one or more steps not shown may be included. In someembodiments, thresholds and/or filters may be applied in connection withone or more of the steps described above in order to obtain candidatedescriptors having desired characteristics or qualities. In someembodiments, one or more of the methods (or portions thereof) may becombined. For example, the method of FIG. 5 may execute and if themethod of FIG. 5 does not generate any results (or results above somethreshold value), the method of FIG. 6 may be executed to increase thenumber of results. Different thresholds or constraints may be used inconnection with one or more of the methods.

FIG. 7 illustrates an example of descriptive data, such as a transcript700 that may be used in connection with one or more of the methods,flows, or environments described herein. Transcript 700 may correspondto any number of video segments. For illustrative purposes, a firstportion 706 a of transcript 700 may be associated with a first videosegment and a second portion 706 b of transcript 700 may be associatedwith a second video segment. The first and second video segments may beassociated with a common show or program, and the transcript 700 may bea closed-captioned textual transcript of the show or program. Theprogram can be segmented in a variety of ways. For example, segments canbe time based. The program may have commercial breaks, and the start andend times of the program between commercial breaks can define a segment.Even between commercial breaks, a program can be segmented to identifytime ranges where different events occur (e.g., a news program maydiscuss tax reform in the first 5 minutes of a 10-minute segment betweencommercials, and then an unsolved crime in the second 5-minutes of the10-minute segment, and each of those 5-minute portions can besub-segments within the 10-minute segment). In some embodiments, thesegments can be based on lines of dialogue in the transcript. Forexample, the first 10 lines of dialogue may be associated with the taxreform store, while the next 15 lines of dialogue can contain thediscussion of the unsolved crime. These segments may be identified bythe program's producers when supplying the program to the systemdescribed herein.

First portion 706 a may include text 714 a corresponding to a writtentranscription of words spoken during the first segment of the video.First portion 706 a may also include OCR'd text 720 a corresponding tographics, banners, and the like shown during the first segment, if theOCR is performed prior to preparation of the transcript. Alternatively,the graphics and banners can simply be part of the video imagery of theshow or program, and the OCR can be conducted by the system herein.

Second portion 706 b may include text 714 b corresponding to a writtentranscription of words spoken during the second segment of the video.Second portion 706 b may include OCR'd text 720 b corresponding tographics, banners, and the like shown during the second segment.

Also shown in FIG. 7 are tag(s) 732 a and 732 b. Tag(s) 732 a may beassociated with first portion 706 a. Tag(s) 732 b may be associated withsecond portion 706 b. While shown separately in FIG. 7, on someembodiments, one or more tags (e.g., one or both of tag(s) 732 a and 732b) may be included in a transcript (e.g., transcript 700). In someembodiments, a tag may comprise a selection of keywords that a person(e.g., a show's producer) selected to summarize the contents of aparticular segment. A tag may be a one-sentence description that may beused in connection with an electronic program guide (EPG) description ofthe show, or can be one or more isolated keywords or phrases identifyingthe subject matter (e.g., racing cars, cooking Alaskan King Crab, etc.).

While some examples have been described above in the context oftelevision technology and communications, aspects of this disclosure mayreadily be applied to, and adapted to be operative on, one or more othercommunication systems. Those communication systems may include computernetworks, satellite networks, cellular networks, and the like.

Although not required, various aspects described herein may be embodiedas a method, a system, and/or as a transitory and/or non-transitorycomputer-readable medium storing executable instructions. Accordingly,those aspects may take the form of an entirely hardware embodiment, anentirely software embodiment, an entirely firmware embodiment, or anembodiment combining software, firmware, and/or hardware. Thefunctionality may be resident in a single computing device orapplication, or may be distributed across multiple computingdevices/platforms or applications, the multiple computingdevices/platforms optionally being connected to one another via one ormore networks. Moreover, the structural components described herein maybe distributed amongst one or more devices, optionally within a commonhousing or casing.

Various signals representing content, data, or events as describedherein may be transferred between a source and a destination in the formof electromagnetic waves traveling through signal-conducting media suchas metal wires, optical fibers, and/or wireless transmission media(e.g., air and/or space).

The various methods and acts may be operative across one or morecomputing servers and one or more networks. The functionality may bedistributed in any manner, or may be located in a single computingdevice (e.g., a server, a client computer, etc.). As described herein, adescriptor may be generated and applied to one or more segments ofcontent (e.g., video). The descriptor may be based at least in part onone or more of a teaser, an OCR technique, and a salient word, term, orphrase.

The methodological acts and processes described herein may be tied toparticular machines or apparatuses. For example, as described herein, adescriptor may be generated at one or more devices, such as servers anduser devices. More generally, one or more apparatuses may include one ormore processors and memory storing instructions, that when executed,cause the one or more apparatuses to perform the methodological acts andprocesses described herein. Furthermore, the methodological acts andprocesses described herein may perform a variety of functions includingtransforming an article (e.g., a transcript and a content segment) intoa different state or thing (e.g., a content segment with an associateddescriptor).

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. While illustrative systems and methods as describedherein embodying various aspects of the present disclosure are shown, itwill be understood by those skilled in the art, that the disclosure isnot limited to these embodiments. Modifications may be made by thoseskilled in the art, particularly in light of the foregoing teachings.For example, each of the features of the aforementioned illustrativeexamples may be utilized alone or in combination or subcombination withelements of the other examples. For example, any of the above describedsystems and methods or parts thereof may be combined with the othermethods and systems or parts thereof described above in any order. Itwill also be appreciated and understood that modifications may be madewithout departing from the true spirit and scope of the presentdisclosure. The description is thus to be regarded as illustrativeinstead of restrictive on the present disclosure.

What is claimed is:
 1. A method comprising: receiving, by a computingdevice, descriptive data associated with: a first segment of a contentitem, and a second segment of the content item; comparing, by thecomputing device and based at least in part on the descriptive data,first segment words with second segment words, wherein the first segmentwords comprise a plurality of words from the first segment and thesecond segment words comprise one or more words from the second segment;determining, by the computing device, that a commercial break, prior tothe second segment, satisfies a threshold distance with respect to thefirst segment words; determining, by the computing device, and based atleast in part on the comparing and the commercial break satisfying thethreshold distance, that the first segment words are descriptive of thesecond segment; determining, by the computing device and based on thedetermining that the first segment words are descriptive of the secondsegment, the first segment words as a candidate descriptor of the secondsegment; and storing, in memory, an indication of an association betweenthe candidate descriptor and the second segment.
 2. The method of claim1, wherein the determining that the first segment words are descriptiveof the second segment comprises determining that the first segment wordsare descriptive of the second segment based at least in part on salienttag detection and a threshold.
 3. The method of claim 1, furthercomprising: iteratively comparing the second segment words with aplurality of other words from the first segment; determining, based onthe iteratively comparing, that one or more of the other words aredescriptive of the second segment; and based on the one or more of theother words being descriptive of the second segment, determining the oneor more of the other words as an another candidate descriptor of thesecond segment.
 4. The method of claim 1, wherein the descriptive datacomprises a transcript that is associated with at least: the firstsegment, the second segment, and a third segment of the content item,the method further comprising: comparing third segment words with thesecond segment words, wherein the third segment words comprise one ormore words included in the third segment; determining that the thirdsegment words are descriptive of the second segment; and based ondetermining that the third segment words are descriptive of the secondsegment, determining the third segment words as a second candidatedescriptor of the second segment.
 5. The method of claim 4, furthercomprising generating, using an Optical Character Recognition technique,the transcript.
 6. The method of claim 1, wherein the determining thatthe first segment words are descriptive of the second segment comprisesdetermining that the first segment words are descriptive of the secondsegment based on a normalization of a result generated by the comparing.7. The method of claim 1, wherein the first segment words comprise atleast one of a phrase or a sentence, and wherein the determining thatthe first segment words are descriptive of the second segment comprisesdetermining that the first segment words are descriptive of the secondsegment based at least in part on the first segment being longer than athreshold.
 8. The method of claim 1, further comprising determining arelationship strength between the descriptive data for the first segmentand descriptive data for one or more additional segments within athreshold proximity to the first segment; and determining the firstsegment words based on the relationship strength being below a maximumthreshold.
 9. The method of claim 1, wherein the first segment is ateaser for the second segment.
 10. A method comprising: receiving, by acomputing device, a transcript for at least: a first segment of acontent item, and a second segment of the content item; comparing, bythe computing device and based on the transcript, first segment wordswith second segment words, wherein the first segment words comprise aplurality of words from the first segment and the second segment wordscomprise one or more words from the second segment; determining, by thecomputing device, that a commercial break, prior to the second segment,satisfies a threshold distance with respect to the first segment words;determining, by the computing device, and based at least in part on thecomparing and the commercial break satisfying the threshold distance,that the first segment words are descriptive of the second segment;determining, by the computing device and based on the determining thatthe first segment words are descriptive of the second segment, the firstsegment words as a candidate descriptor of the second segment; andstoring, in memory, an indication of an association between thecandidate descriptor and the second segment.
 11. The method of claim 10,wherein the determining that the first segment words are descriptive ofthe second segment comprises determining that the first segment wordsare descriptive of the second segment based at least in part on salienttag detection and a threshold.
 12. The method of claim 10, furthercomprising: iteratively comparing the second segment words with aplurality of other words from the first segment; determining, based onthe iteratively comparing, that one or more of the other words aredescriptive of the second segment; and determining, based on the one ormore of the other words being descriptive of the second segment, the oneor more of the other words as an another candidate descriptor of thesecond segment.
 13. The method of claim 10, further comprising:comparing third segment words with the second segment words, wherein thethird segment words comprise one or more words included in a thirdsegment; determining that the third segment words are descriptive of thesecond segment; and based on the determining that the third segmentwords are descriptive of the second segment, determining the thirdsegment words as a second candidate descriptor of the second segment.14. The method of claim 10, wherein the determining that the firstsegment words are descriptive of the second segment comprisesdetermining that the first segment words are descriptive of the secondsegment based on a normalization of a result generated by the comparing.15. The method of claim 10, wherein the first segment words comprise atleast one of a phrase or a sentence, and wherein the determining thatthe first segment words are descriptive of the second segment comprisesdetermining that the first segment words are descriptive of the secondsegment based at least in part on the first segment being longer than athreshold.
 16. The method of claim 10, further comprising determining arelationship strength between the transcript for the first segment andone or more additional segments within a threshold proximity to thefirst segment; and determining the first segment words based on therelationship strength being below a maximum threshold.
 17. The method ofclaim 10, wherein the first segment is a teaser for the second segment.18. A method comprising: receiving, by a computing device, descriptivedata associated with: a first segment of a content item, and a secondsegment of the content item; determining, by the computing device andbased at least in part on the descriptive data, a relationship strengthbased on comparing first segment words with second segment words,wherein the first segment words comprise a plurality of words from thefirst segment and the second segment words comprise one or more wordsfrom the second segment; determining, by the computing device, based ona commercial break, prior to the second segment, satisfying a thresholddistance with respect to the first segment words, and based on therelationship strength exceeding a minimum threshold, that the firstsegment words are descriptive of the second segment; assigning, by thecomputing device and based on the determining that the first segmentwords are descriptive of the second segment, the first segment words asa candidate descriptor of the second segment; and storing, in memory, anindication of an association between the candidate descriptor and thesecond segment.
 19. The method of claim 18, wherein the descriptive datacomprises a transcript, and further comprising: receiving, by thecomputing device, a tag assigned to the content item; iterativelycomparing, by the computing device, the transcript and the tag; anddetermining, by the computing device, and based on the iterativelycomparing, whether to add one or more words of the transcript to acandidate descriptor for the content item.
 20. The method of claim 18,wherein the first segment is a teaser for the second segment.